Point Cloud Data Processing via Octree LOD and Tile Segmentation
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Solution Overview
Problem
Existing methods for processing point cloud data are inefficient, leading to latency and complexity in encoding and decoding, which hinders the delivery of high-quality point cloud services for applications like virtual reality, augmented reality, and self-driving services.
Innovation Solution
A method involving the encoding of point cloud data and its transmission as a bitstream, followed by decoding at the receiving end, to efficiently process and render point cloud content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is processed using existing methods, then the processing can be performed, but the processing efficiency is low and latency is high
Solution Approach 1:
The patent segments point cloud data into multiple tiles or partitions, allowing parallel processing of different segments. This segmentation enables the system to process large point cloud datasets more efficiently by dividing the workload across multiple processing units, thereby improving productivity and reducing overall processing latency.
Solution Approach 2:
The patent performs preliminary actions by pre-processing point cloud data during encoding, including organizing data into tiles, performing predictive coding, and preparing transformation coefficients in advance. This preliminary processing during the encoding phase reduces the computational burden during real-time decoding and rendering, thus reducing processing latency.
2Productivity
If point cloud data is processed using existing methods, then the processing can be performed, but the encoding and decoding complexity is high
Solution Approach 1:
By segmenting point cloud data into tiles and processing each tile independently with standardized algorithms, the patent reduces encoding and decoding complexity. Each segment can be processed using the same simplified procedures, avoiding the need for complex global processing while maintaining high service delivery quality.
Solution Approach 2:
The patent changes processing parameters by using predictive coding models that adapt to local point cloud characteristics, adjusting prediction orders and transformation types based on data density and geometry. This parameter adaptation simplifies the overall encoding and decoding processes while maintaining high productivity and service quality.
3Manufacturing precision
If tens of thousands to hundreds of thousands of point data are used to represent point cloud content, then the content quality is maintained, but the processing burden increases significantly
Solution Approach 1:
The patent extracts only the essential geometric and attribute information from dense point cloud data during encoding, representing complex point clouds with compressed data structures that retain quality. This extraction of key features maintains manufacturing precision while significantly reducing processing complexity for rendering and manipulation.
Solution Approach 2:
By segmenting dense point cloud data into manageable tiles and applying hierarchical processing, the patent maintains content quality through selective processing of different detail levels. This segmentation allows the system to handle hundreds of thousands of points by processing them in organized groups, reducing overall processing complexity while preserving quality.
Data Source
AI summary
A point cloud data processing method according to embodiments may comprise a step of encoding and transmitting point cloud data, wherein said encoding comprises encoding a geometry indicating the positions of one or more points of the point cloud data, the step of encoding a geometry further comprises a step of generating an octree of the geometry, and a step of encoding attributes of the one or more points comprises the steps of: generating Morton codes of the one or more points; and rearranging the one or more points on the basis of the octree to generate one or more levels of detail (LOD). The point cloud data processing method according to embodiments may comprise receiving and decoding point cloud data.